"""Engine: аудио → текст на базе Whisper base (CTranslate2).

Модель не загружает torch — инференс через ctranslate2 + faster-whisper.
"""

from __future__ import annotations

from pathlib import Path
from typing import Any, Dict, List, Optional, Union

from faster_whisper import WhisperModel

DEFAULT_MODEL_DIR = Path(__file__).resolve().parent.parent / "model"


class AuditEngine:
    """Расшифровщик речи в текст (ASR, 99 языков)."""

    def __init__(
        self,
        model_dir: Optional[Union[str, Path]] = None,
        device: str = "cpu",
        compute_type: str = "int8",
        cpu_threads: int = 0,
    ) -> None:
        model_dir = Path(model_dir or DEFAULT_MODEL_DIR)
        if not (model_dir / "model.bin").exists():
            raise FileNotFoundError(
                f"Веса модели не найдены: {model_dir}/model.bin\n"
                "Запустите сначала: python scripts/build_model.py"
            )
        self.model_dir = model_dir
        self.model = WhisperModel(
            str(model_dir),
            device=device,
            compute_type=compute_type,
            cpu_threads=cpu_threads,
        )

    def transcribe(
        self,
        audio: Union[str, Path],
        language: Optional[str] = None,
        beam_size: int = 5,
        vad_filter: bool = False,
    ) -> Dict[str, Any]:
        """Транскрибировать аудио. Возвращает dict с текстом и метаданными."""
        segments, info = self.model.transcribe(
            str(audio),
            language=language,
            beam_size=beam_size,
            vad_filter=vad_filter,
        )
        parts: List[Dict[str, Any]] = []
        for seg in segments:
            parts.append(
                {
                    "start": round(float(seg.start), 2),
                    "end": round(float(seg.end), 2),
                    "text": seg.text.strip(),
                }
            )
        return {
            "text": " ".join(p["text"] for p in parts).strip(),
            "language": info.language,
            "language_probability": round(float(info.language_probability), 4),
            "duration": round(float(info.duration), 2),
            "segments": parts,
        }